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Dealing with imbalanced and weakly l...

Vluymans, Sarah.

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Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods/ by Sarah Vluymans.
Author:	Vluymans, Sarah.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xviii, 249 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Classiﬁcation -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classiﬁcation of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography.
Contained By:	Springer eBooks
Subject:	Fuzzy sets. -
Online resource:	https://doi.org/10.1007/978-3-030-04663-7
ISBN:	9783030046637

Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods
Vluymans, Sarah.

Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods[electronic resource] /by Sarah Vluymans. - Cham :Springer International Publishing :2019. - xviii, 249 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8071860-949X ;. - Studies in computational intelligence ;v.807..

Introduction -- Classiﬁcation -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classiﬁcation of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography.

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.

ISBN: 9783030046637

Standard No.: 10.1007/978-3-030-04663-7doiSubjects--Topical Terms:

562997
Fuzzy sets.

LC Class. No.: QA248.5 / .V58 2019

Dewey Class. No.: 511.3223

Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods
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100 1 $a Vluymans, Sarah. $3 3381754
245 1 0 $a Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods $h [electronic resource] / $c by Sarah Vluymans.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xviii, 249 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.807
505 0 $a Introduction -- Classiﬁcation -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classiﬁcation of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography.
520 $a This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.
650 0 $a Fuzzy sets. $3 562997
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
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830 0 $a Studies in computational intelligence ; $v v.807. $3 3381755
856 4 0 $u https://doi.org/10.1007/978-3-030-04663-7
950 $a Intelligent Technologies and Robotics (Springer-42732)